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    <p>Reminder for the Data Science talk tomorrow Friday, at noon in
      the MRB seminar room.</p>
    <p>Please use the link below to register if you plan to attend.</p>
    <p>best,</p>
    <p>V. Tsotras<br>
    </p>
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      <br>
      -------- Forwarded Message --------
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            <th valign="BASELINE" nowrap="nowrap" align="RIGHT">Subject:
            </th>
            <td>[UCR_DataScience] Data Science talk by Prof. Maziar
              Raissi, Friday May 10th, 12-1pm, MRB Seminar Room</td>
          </tr>
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            <th valign="BASELINE" nowrap="nowrap" align="RIGHT">Date: </th>
            <td>Tue, 7 May 2024 09:23:15 -0700</td>
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            <th valign="BASELINE" nowrap="nowrap" align="RIGHT">From: </th>
            <td>tsotras--- via DataScience
              <a class="moz-txt-link-rfc2396E" href="mailto:datascience@lists.ucr.edu"><datascience@lists.ucr.edu></a></td>
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            <th valign="BASELINE" nowrap="nowrap" align="RIGHT">Reply-To:
            </th>
            <td><a class="moz-txt-link-abbreviated" href="mailto:tsotras@cs.ucr.edu">tsotras@cs.ucr.edu</a></td>
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            <th valign="BASELINE" nowrap="nowrap" align="RIGHT">To: </th>
            <td><a class="moz-txt-link-abbreviated" href="mailto:datascience@lists.ucr.edu">datascience@lists.ucr.edu</a></td>
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      <br>
      <br>
      We will have a Data Science seminar this Friday, May 10th,
      12:00-1:00pm at<br>
      the MRB Seminar Room (1st floor).<br>
      <br>
      **** Pizza and refreshments will be provided ****<br>
      <br>
      To keep track of the number of attendees, please *register* at:<br>
<a class="moz-txt-link-freetext" href="https://www.eventbrite.com/e/data-science-talk-tickets-900165367847">https://www.eventbrite.com/e/data-science-talk-tickets-900165367847</a><br>
      <br>
      The talk will be given by Prof. Maziar Raissi, Mathematics
      Department, UCR<br>
      <br>
      TITLE:<br>
      "Data-Efficient Deep Learning using Physics-Informed Neural
      Networks"<br>
      <br>
      <br>
      ABSTRACT:<br>
      A grand challenge with great opportunities is to develop a
      coherent<br>
      framework that enables blending conservation laws, physical
      principles,<br>
      and/or phenomenological behaviours expressed by differential
      equations<br>
      with the vast data sets available in many fields of engineering,
      science,<br>
      and technology. At the intersection of probabilistic machine
      learning,<br>
      deep learning, and scientific computations, this work is pursuing
      the<br>
      overall vision to establish promising new directions for
      harnessing the<br>
      long-standing developments of classical methods in applied
      mathematics and<br>
      mathematical physics to design learning machines with the ability
      to<br>
      operate in complex domains without requiring large quantities of
      data. To<br>
      materialize this vision, this work is exploring two complementary<br>
      directions: (1) designing data-efficient learning machines capable
      of<br>
      leveraging the underlying laws of physics, expressed by time
      dependent and<br>
      non-linear differential equations, to extract patterns from<br>
      high-dimensional data generated from experiments, and (2)
      designing novel<br>
      numerical algorithms that can seamlessly blend equations and noisy<br>
      multi-fidelity data, infer latent quantities of interest (e.g.,
      the<br>
      solution to a differential equation), and naturally quantify
      uncertainty<br>
      in computations.<br>
      <br>
      <br>
      ------------------------------------<br>
      Sponsored by the UCR Data Science Center, the purpose of the Data
      Science<br>
      Seminars is to foster collaborations between "core" Data Science
      faculty<br>
      (from CSE/ECE/Stat Departments) and faculty/visitors from other
      sciences<br>
      that face Data Science problems in their research. These informal<br>
      gatherings are open to interested faculty and graduate students.
      Each<br>
      meeting will start with a talk describing research problems and
      then a<br>
      discussion will follow for questions, open problems, ideas for
      possible<br>
      collaborations etc.<br>
      <br>
      A full list of previous seminars appears at:<br>
      <a class="moz-txt-link-freetext" href="http://datascience.ucr.edu/seminars">http://datascience.ucr.edu/seminars</a><br>
      <br>
      Forward this email to other colleagues or graduate students in
      your lab<br>
      that may be interested. Moreover, if you are interested in giving
      a Data<br>
      Science related talk, please contact me (<a class="moz-txt-link-abbreviated" href="mailto:tsotras@cs.ucr.edu">tsotras@cs.ucr.edu</a>).<br>
      <br>
      Sincerely,<br>
      Vassilis Tsotras<br>
      Professor, CSE Department<br>
      co-Director, Data Science Major<br>
      Director, MS in Computational Data Science<br>
      <br>
      <br>
      _______________________________________________<br>
      DataScience mailing list<br>
      <a class="moz-txt-link-abbreviated" href="mailto:DataScience@lists.ucr.edu">DataScience@lists.ucr.edu</a><br>
      <a class="moz-txt-link-freetext" href="https://lists.ucr.edu/mailman/listinfo/datascience">https://lists.ucr.edu/mailman/listinfo/datascience</a><br>
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